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E-raamat: Advances in Longitudinal Survey Methodology [Wiley Online]

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Advances in Longitudinal Survey MethodologySuurem pilt
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Advances in Longitudinal Survey Methodology Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodology

Advances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, Methodology of Longitudinal Surveys, published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting.

New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents consent to data linkage add to the books relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of:

A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequency An exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studies An examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement. An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.
List of Contributors
xvii
Preface xxiii
About the Companion Website xxvii
1 Refreshment Sampling For Longitudinal Surveys
1(25)
Nicole Watson
Peter Lynn
1.1 Introduction
1(5)
1.2 Principles
6(1)
1.3 Sampling
7(6)
1.3.1 Sampling Frame
7(1)
1.3.2 Screening
8(2)
1.3.3 Sample Design
10(1)
1.3.4 Questionnaire Design
10(1)
1.3.5 Frequency
11(2)
1.4 Recruitment
13(1)
1.5 Data Integration
14(1)
1.6 Weighting
15(3)
1.7 Impact on Analysis
18(2)
1.8 Conclusions
20(6)
References
22(4)
2 Collecting Biomarker Data In Longitudinal Surveys
26(21)
Meena Kumari
Michaela Benzeval
2.1 Introduction
26(1)
2.2 What Are Biomarkers, and Why Are They of Value?
27(5)
2.2.1 Detailed Measurements of Ill Health
28(1)
2.2.2 Biological Pathways
29(2)
2.2.3 Genetics in Longitudinal Studies
31(1)
2.3 Approaches to Collecting Biomarker Data in Longitudinal Studies
32(8)
2.3.1 Consistency and Relevance of Measures Over Time
33(2)
2.3.2 Panel Conditioning and Feedback
35(1)
2.3.3 Choices of When and Who to Ask for Sensitive or Invasive Measures
36(3)
2.3.4 Cost
39(1)
2.4 The Future
40(7)
References
42(5)
3 Innovations In Participant Engagement And Tracking In Longitudinal Surveys
47(27)
Lisa Calderwood
Matt Brown
Emily Gilbert
Erica Wong
3.1 Introduction and Background
47(1)
3.2 Literature Review
48(4)
3.3 Current Practice
52(3)
3.4 New Evidence on Internet and Social Media for Participant Engagement
55(3)
3.4.1 Background
55(1)
3.4.2 Findings
56(1)
3.4.2.1 MCS
56(1)
3.4.2.2 Next Steps
57(1)
3.4.3 Summary and Conclusions
58(1)
3.5 New Evidence on Internet and Social Media for Tracking
58(4)
3.5.1 Background
58(2)
3.5.2 Findings
60(1)
3.5.3 Summary and Conclusions
61(1)
3.6 New Evidence on Administrative Data for Tracking
62(6)
3.6.1 Background
62(1)
3.6.2 Findings
63(4)
3.6.3 Summary and Conclusions
67(1)
3.7 Conclusion
68(6)
Acknowledgements
69(1)
References
69(5)
4 Effects On Panel Attrition And Fieldwork Outcomes From Selection For A Supplemental Study: Evidence From The Panel Study Of Income Dynamics
74(26)
Narayan Sastry
Paula Fomby
Katherine A. McGonagle
4.1 Introduction
74(1)
4.2 Conceptual Framework
75(2)
4.3 Previous Research
77(1)
4.4 Data and Methods
78(8)
4.5 Results
86(9)
4.6 Conclusions
95(5)
Acknowledgements
98(1)
References
98(2)
5 The Effects Of Biological Data Collection In Longitudinal Surveys On Subsequent Wave Cooperation
100(22)
Fiona Pashazadeh
Alexandru Cernat
Joseph W. Sakshaug
5.1 Introduction
100(1)
5.2 Literature Review
101(5)
5.3 Biological Data Collection and Subsequent Cooperation: Research Questions
106(2)
5.4 Data
108(1)
5.5 Modelling Steps
109(1)
5.6 Results
110(4)
5.7 Discussion and Conclusion
114(2)
5.8 Implications for Survey Researchers
116(6)
References
117(5)
6 Understanding Data Linkage Consent In Longitudinal Surveys
122(29)
Annette Jackie
Kelsey Beninger
Jonathan Burton
Mick P. Couper
6.1 Introduction
122(3)
6.2 Quantitative Research: Consistency of Consent and Effect of Mode of Data Collection
125(11)
6.2.1 Data and Methods
125(3)
6.2.2 Results
128(1)
6.2.2.1 How Consistent Are Respondents about Giving Consent to Data Linkage between Topics?
128(2)
6.2.2.2 How Consistent Are Respondents about Giving Consent to Data Linkage over Time?
130(1)
6.2.2.3 Does Consistency over Time Vary between Domains?
131(1)
6.2.2.4 What Is the Effect of Survey Mode on Consent?
132(4)
6.3 Qualitative Research: How Do Respondents Decide Whether to Give Consent to Linkage?
136(9)
6.3.1 Methods
136(1)
6.3.2 Results
137(1)
6.3.2.1 How Do Participants Interpret Consent Questions?
137(4)
6.3.2.2 What Do Participants Think Are the Implications of Giving Consent to Linkage?
141(1)
6.3.2.3 What Influences the Participant's Decision Whether or Not to Give Consent?
142(2)
6.3.2.4 How Does the Survey Mode Influence the Decision to Consent?
144(1)
6.3.2.5 Why Do Participants Change their Consent Decision over Time?
144(1)
6.4 Discussion
145(6)
Acknowledgements
147(1)
References
148(3)
7 Determinants Of Consent To Administrative Records Linkage In Longitudinal Surveys: Evidence From Next Steps
151(30)
Darina Peycheva
George Ploubidis
Lisa Calderwood
7.1 Introduction
151(2)
7.2 Literature Review
153(2)
7.3 Data and Methods
155(5)
7.3.1 About the Study
155(1)
7.3.2 Consents Sought and Consent Procedure
156(1)
7.3.3 Analytic Sample
157(1)
7.3.4 Methods
158(2)
7.4 Results
160(13)
7.4.1 Consent Rates
160(3)
7.4.2 Regression Models
163(1)
7.4.2.1 Concepts and Variables
163(1)
7.4.2.2 Characteristics Related to All or Most Consent Domains
164(1)
7.4.2.3 National Health Service (NHS) Records
164(3)
7.4.2.4 Police National Computer (PNC) Criminal Records
167(1)
7.4.2.5 Education Records
167(3)
7.4.2.6 Economic Records
170(3)
7.5 Discussion
173(8)
7.5.1 Summary of Results
173(3)
7.5.2 Methodological Considerations and Limitations
176(1)
7.5.3 Practical Implications
177(1)
References
177(4)
8 Consent To Data Linkage: Experimental Evidence From An Online Panel
181(23)
Ben Edwards
Nicholas Biddle
8.1 Introduction
181(1)
8.2 Background
182(4)
8.2.1 Experimental Studies of Data Linkage Consent in Longitudinal Surveys
183(3)
8.3 Research Questions
186(1)
8.4 Method
187(3)
8.4.1 Data
187(1)
8.4.2 Study 1: Attrition Following Data Linkage Consent
187(1)
8.4.3 Study 2: Testing the Effect of Type and Length of Data Linkage Consent Questions
188(2)
8.5 Results
190(8)
8.5.1 Do Requests for Data Linkage Consent Affect Response Rates in Subsequent Waves? (RQ1)
190(1)
8.5.2 Do Consent Rates Depend on Type of Data Linkage Requested? (RQ2a)
191(2)
8.5.3 Do Consent Rates Depend on Survey Mode? (RQ2b)
193(1)
8.5.4 Do Consent Rates Depend on the Length of the Request? (RQ2c)
193(1)
8.5.5 Effects on Understanding of the Data Linkage Process (RQ3)
194(3)
8.5.6 Effects on Perceptions of the Risk of Data Linkage (RQ4)
197(1)
8.6 Discussion
198(6)
References
200(4)
9 Mixing Modes In Household Panel Surveys: Recent Developments And New Findings
204(23)
Marieke Voorpostel
Oliver Lipps
Caroline Roberts
9.1 Introduction
204(1)
9.2 The Challenges of Mixing Modes in Household Panel Surveys
205(2)
9.3 Current Experiences with Mixing Modes in Longitudinal Household Panels
207(7)
9.3.1 The German Socio-Economic Panel (SOEP)
207(1)
9.3.2 The Household, Income, and Labour Dynamics in Australia (HILDA) Survey
208(1)
9.3.3 The Panel Study of Income Dynamics (PSID)
209(2)
9.3.4 The UK Household Longitudinal Study (UKHLS)
211(1)
9.3.5 The Korean Labour and Income Panel Study (KLIPS)
212(1)
9.3.6 The Swiss Household Panel (SHP)
213(1)
9.4 The Mixed-Mode Pilot of the Swiss Household Panel Study
214(9)
9.4.1 Design of the SHP Pilot
214(3)
9.4.2 Results of the First Wave
217(1)
9.4.2.1 Overall Response Rates in the Three Groups
217(1)
9.4.2.2 Use of Different Modes in the Three Groups
217(2)
9.4.2.3 Household Nonresponse in the Three Groups
219(2)
9.4.2.4 Individual Nonresponse in the Three Groups
221(2)
9.5 Conclusion
223(4)
References
224(3)
10 Estimating The Measurement Effects Of Mixed Modes In Longitudinal Studies: Current Practice And Issues
227(23)
Alexandru Cernat
Joseph W. Sakshaug
10.1 Introduction
227(3)
10.2 Types of Mixed-Mode Designs
230(2)
10.3 Mode Effects and Longitudinal Data
232(5)
10.3.1 Estimating Change from Mixed-Mode Longitudinal Survey Data
233(1)
10.3.2 General Concepts in the Investigation of Mode Effects
233(2)
10.3.3 Mode Effects on Measurement in Longitudinal Data: Literature Review
235(2)
10.4 Methods for Estimating Mode Effects on Measurement in Longitudinal Studies
237(2)
10.5 Using Structural Equation Modelling to Investigate Mode Differences in Measurement
239(6)
10.6 Conclusion
245(5)
Acknowledgement
246(1)
References
246(4)
11 Measuring Cognition In A Multi-Mode Context
250(22)
Mary Beth Ofstedal
Colleen A. Mcclain
Mick P. Couper
11.1 Introduction
250(1)
11.2 Motivation and Previous Literature
251(5)
11.2.1 Measurement of Cognition in Surveys
251(1)
11.2.2 Mode Effects and Survey Response
252(1)
11.2.3 Cognition in a Multi-Mode Context
252(2)
11.2.4 Existing Mode Comparisons of Cognitive Ability
254(2)
11.3 Data and Methods
256(5)
11.3.1 Data Source
256(1)
11.3.2 Analytic Sample
256(1)
11.3.3 Administration of Cognitive Tests
257(1)
11.3.4 Methods
258(1)
11.3.4.1 Item Missing Data
259(1)
11.3.4.2 Completion Time
259(1)
11.3.4.3 Overall Differences in Scores
259(1)
11.3.4.4 Correlations Between Measures
259(1)
11.3.4.5 Trajectories over Time
260(1)
11.3.4.6 Models Predicting Cognition as an Outcome
260(1)
11.4 Results
261(5)
11.4.1 Item-Missing Data
261(1)
11.4.2 Completion Time
262(1)
11.4.3 Differences in Mean Scores
262(1)
11.4.4 Correlations Between Measures
263(1)
11.4.5 Trajectories over Time
263(2)
11.4.6 Substantive Models
265(1)
11.5 Discussion
266(6)
Acknowledgements
268(1)
References
268(4)
12 Panel Conditioning: Types, Causes, And Empirical Evidence Of What We Know So Far
272(30)
Bella Struminskaya
Michael Bosnjak
12.1 Introduction
272(1)
12.2 Methods for Studying Panel Conditioning
273(3)
12.3 Mechanisms of Panel Conditioning
276(16)
12.3.1 Survey Response Process and the Effects of Repeated Interviewing
276(3)
12.3.2 Reflection/Cognitive Stimulus
279(1)
12.3.3 Empirical Evidence of Reflection/Cognitive Stimulus
280(1)
12.3.3.1 Changes in Attitudes Due to Reflection
280(2)
12.3.3.2 Changes in (Self-Reported) Behaviour Due to Reflection
282(2)
12.3.3.3 Changes in Knowledge Due to Reflection
284(1)
12.3.4 Social Desirability Reduction
285(1)
12.3.5 Empirical Evidence of Social Desirability Effects
285(2)
12.3.6 Satisficing
287(1)
12.3.7 Empirical Evidence of Satisficing
288(1)
12.3.7.1 Misreporting to Filter Questions as a Conditioning Effect Due to Satisficing
288(1)
12.3.7.2 Misreporting to More Complex Filter (Looping) Questions
289(1)
12.3.7.3 Within-Interview and Between-Waves Conditioning in Filter Questions
290(2)
12.4 Conclusion and Implications for Survey Practice
292(10)
References
295(7)
13 Interviewer Effects In Panel Surveys
302(35)
Simon Kuhne
Martin Kroh
13.1 Introduction
302(1)
13.2 Motivation and State of Research
303(10)
13.2.1 Sources of Interviewer-Related Measurement Error
303(1)
13.2.1.1 Interviewer Deviations
304(1)
13.2.1.2 Social Desirability
305(2)
13.2.1.3 Priming
307(1)
13.2.2 Moderating Factors of Interviewer Effects
307(1)
13.2.3 Interviewer Effects in Panel Surveys
308(2)
13.2.4 Identifying Interviewer Effects
310(1)
13.2.4.1 Interviewer Variance
310(1)
13.2.4.2 Interviewer Bias
311(1)
13.2.4.3 Using Panel Data to Identify Interviewer Effects
312(1)
13.3 Data
313(1)
13.3.1 The Socio-Economic Panel
313(1)
13.3.2 Variables
314(1)
13.4 The Size and Direction of Interviewer Effects in Panels
314(8)
13.4.1 Methods
314(4)
13.4.2 Results
318(2)
13.4.3 Effects on Precision
320(1)
13.4.4 Effects on Validity
321(1)
13.5 Dynamics of Interviewer Effects in Panels
322(4)
13.5.1 Methods
324(1)
13.5.2 Results
324(1)
13.5.2.1 Interviewer Variance
324(1)
13.5.2.2 Interviewer Bias
325(1)
13.6 Summary and Discussion
326(11)
References
329(8)
14 Improving Survey Measurement Of Household Finances: A Review Of New Data Sources And Technologies
337(31)
Annette Jackie
Mick P. Couper
Alessandra Gaia
Carli Lessof
14.1 Introduction
337(4)
14.1.1 Why Is Good Financial Data Important for Longitudinal Surveys?
338(1)
14.1.2 Why New Data Sources and Technologies for Longitudinal Surveys?
339(1)
14.1.3 How Can New Technologies Change the Measurement Landscape?
340(1)
14.2 The Total Survey Error Framework
341(2)
14.3 Review of New Data Sources and Technologies
343(9)
14.3.1 Financial Aggregators
346(1)
14.3.2 Loyalty Card Data
346(1)
14.3.3 Credit and Debit Card Data
347(1)
14.3.4 Credit Rating Data
348(1)
14.3.5 In-Home Scanning of Barcodes
349(1)
14.3.6 Scanning of Receipts
350(1)
14.3.7 Mobile Applications and Expenditure Diaries
350(2)
14.4 New Data Sources and Technologies and TSE
352(6)
14.4.1 Errors of Representation
352(1)
14.4.1.1 Coverage Error
352(1)
14.4.1.2 Non-Participation Error
353(2)
14.4.2 Measurement Error
355(1)
14.4.2.1 Specification Error
355(1)
14.4.2.2 Missing or Duplicate Items/Episodes
356(1)
14.4.2.3 Data Capture Error
357(1)
14.4.2.4 Processing or Coding Error
357(1)
14.4.2.5 Conditioning Error
357(1)
14.5 Challenges and Opportunities
358(10)
Acknowledgements
360(1)
References
360(8)
15 How To Pop The Question? Interviewer And Respondent Behaviours When Measuring Change With Proactive Dependent Interviewing
368(31)
Annette Jackie
Torek Al Baghal
Stephanie Eckman
Emanuela Sala
15.1 Introduction
368(2)
15.2 Background
370(4)
15.3 Data
374(2)
15.4 Behaviour Coding Interviewer and Respondent Interactions
376(3)
15.5 Methods
379(1)
15.6 Results
380(8)
15.6.1 Does the DI Wording Affect how Interviewers and Respondents Behave? (RQ1)
381(1)
15.6.2 Does the Wording of DI Questions Affect the Sequences of Interviewer and Respondent Interactions? (RQ2)
382(3)
15.6.3 Which Interviewer Behaviours Lead to Respondents Giving Codeable Answers? (RQ3)
385(1)
15.6.4 Are the Different Rates of Change Measured with Different DI Wordings Explained by Differences in I and R Behaviours? (RQ4)
386(2)
15.7 Conclusion
388(11)
Acknowledgements
390(1)
References
390(9)
16 Assessing Discontinuities And Rotation Group Bias In Rotating Panel Designs
399(25)
Jan A. Van Den Brakel
Paul A. Smith
Duncan Elliott
Sabine Krieg
Timo Schmid
Nikos Tzavidis
16.1 Introduction
399(2)
16.2 Methods for Quantifying Discontinuities
401(1)
16.3 Time Series Models for Rotating Panel Designs
402(6)
16.3.1 Rotating Panels and Rotation Group Bias
402(2)
16.3.2 Structural Time Series Model for Rotating Panels
404(3)
16.3.3 Fitting Structural Time Series Models
407(1)
16.4 Time Series Models for Discontinuities in Rotating Panel Designs
408(4)
16.4.1 Structural Time Series Model for Discontinuities
409(1)
16.4.2 Parallel Run
410(1)
16.4.3 Combining Information from a Parallel Run with the Intervention Model
411(1)
16.4.4 Auxiliary Time Series
412(1)
16.5 Examples
412(7)
16.5.1 Redesigns in the Dutch LFS
412(5)
16.5.2 Using a State Space Model to Assess Redesigns in the UK LFS
417(2)
16.6 Discussion
419(5)
References
421(3)
17 Proper Multiple Imputation Of Clustered Or Panel Data
424(23)
Martin Spiess
Kristian Kleinke
Lost Reinecke
17.1 Introduction
424(1)
17.2 Missing Data Mechanism and Ignorability
425(1)
17.3 Multiple Imputation (MI)
426(8)
17.3.1 Theory and Basic Approaches
426(3)
17.3.2 Single Versus Multiple Imputation
429(1)
17.3.2.1 Unconditional Mean Imputation and Regression Imputation
430(1)
17.3.2.2 Last Observation Carried Forward
430(2)
17.3.2.3 Row-and-Column Imputation
432(2)
17.4 Issues in the Longitudinal Context
434(7)
17.4.1 Single-Level Imputation
435(2)
17.4.2 Multilevel Multiple Imputation
437(2)
17.4.3 Interactions and Non-Linear Associations
439(2)
17.5 Discussion
441(6)
References
443(4)
18 Issues In Weighting For Longitudinal Surveys
447(22)
Peter Lynn
Nicole Watson
18.1 Introduction: The Longitudinal Context
447(4)
18.1.1 Dynamic Study Population
447(1)
18.1.2 Wave Non-Response Patterns
448(1)
18.1.3 Auxiliary Variables
449(1)
18.1.4 Longitudinal Surveys as a Multi-Purpose Research Resource
450(1)
18.1.5 Multiple Samples
450(1)
18.2 Population Dynamics
451(7)
18.2.1 Post-Stratification
451(2)
18.2.2 Population Entrants
453(1)
18.2.3 Uncertain Eligibility
454(4)
18.3 Sample Participation Dynamics
458(5)
18.3.1 Subsets of Instrument Combinations
459(2)
18.3.2 Weights for Each Pair of Instruments
461(1)
18.3.3 Analysis-Specific Weights
462(1)
18.4 Combining Multiple Non-Response Models
463(2)
18.5 Discussion
465(4)
Acknowledgements
466(1)
References
467(2)
19 Small-Area Estimation Of Cross-Classified Gross Flows Using Longitudinal Survey Data
469(22)
Yves Thibaudeau
Eric Slud
Yang Cheng
19.1 Introduction
469(1)
19.2 Role of Model-Assisted Estimation in Small Area Estimation
470(1)
19.3 Data and Methods
471(3)
19.3.1 Data
471(2)
19.3.2 Estimate and Variance Comparisons
473(1)
19.4 Estimating Gross Flows
474(1)
19.5 Models
475(6)
19.5.1 Generalised Logistic Fixed Effect Models
475(1)
19.5.2 Fixed Effect Logistic Models for Estimating Gross Flows
476(1)
19.5.3 Equivalence between Fixed-Effect Logistic Regression and Log-Linear Models
477(1)
19.5.4 Weighted Estimation
478(1)
19.5.5 Mixed-Effect Logit Models for Gross Flows
479(2)
19.5.6 Application to the Estimation of Gross Flows
481(1)
19.6 Results
481(5)
19.6.1 Goodness of Fit Tests for Fixed Effect Models
481(2)
19.6.2 Fixed-Effect Logit-Based Estimation of Gross Flows
483(1)
19.6.3 Mixed Effect Models
483(1)
19.6.4 Comparison of Models through BRR Variance Estimation
483(3)
19.7 Discussion
486(5)
Acknowledgements
488(1)
References
488(3)
20 Nonparametric Estimation For Longitudinal Data With Informative Missingness
491(22)
Zahoor Ahmad
Li-Chun Zhang
20.1 Introduction
491(3)
20.2 Two NEE Estimators of Change
494(3)
20.3 On the Bias of NEE
497(2)
20.4 Variance Estimation
499(2)
20.4.1 NEE (Expression 20.3)
499(1)
20.4.2 NEE (Expression 20.6)
500(1)
20.5 Simulation Study
501(6)
20.5.1 Data
502(1)
20.5.2 Response Probability Models
502(1)
20.5.3 Simulation Set-up
503(1)
20.5.4 Results
504(3)
20.6 Conclusions
507(6)
References
511(2)
Index 513
Peter Lynn is Professor of Survey Methodology and Director of the Institute for Social and Economic Research (ISER), University of Essex. ISER is one of the leading research centres in the world for longitudinal survey methods and Professor Lynn has headed the survey methods programme at ISER since he joined Essex in 2001. Professor Lynn has published more than 60 articles on survey methods topics in top scientific journals, mostly on topics specific to longitudinal surveys, in addition to numerous book chapters, reports and other articles.
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